Esempio n. 1
0
  /**
   * JPASupport instances a and b are equals if either <strong>a == b</strong> or a and b have same
   * </strong>{@link #key key} and class</strong>
   *
   * @param other
   * @return true if equality condition above is verified
   */
  @Override
  public boolean equals(Object other) {
    final Object key = this._key();

    if (other == null) {
      return false;
    }
    if (this == other) {
      return true;
    }
    if (key == null) {
      return false;
    }
    if (play.db.Model.class.isAssignableFrom(other.getClass()) && key.getClass().isArray()) {
      Object otherKey = ((play.db.Model) other)._key();
      if (otherKey.getClass().isArray()) {
        return Arrays.deepEquals((Object[]) key, (Object[]) otherKey);
      }
      return false;
    }

    if (!this.getClass().isAssignableFrom(other.getClass())) {
      return false;
    }

    return key.equals(((play.db.Model) other)._key());
  }
  @Override
  public boolean equals(Object other) {
    if (!(other instanceof SimilarityMatrix)) {
      return false;
    }

    SimilarityMatrix otherMatrix = (SimilarityMatrix) other;

    return otherMatrix.builtFrom.equals(builtFrom)
        && otherMatrix.xSubmissions.equals(xSubmissions)
        && otherMatrix.ySubmissions.equals(ySubmissions)
        && Arrays.deepEquals(otherMatrix.entries, entries);
  }
Esempio n. 3
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    /**
     * Train a Deep Learning model, assumes that all members are populated If checkpoint == null,
     * then start training a new model, otherwise continue from a checkpoint
     */
    public final void buildModel() {
      DeepLearningModel cp = null;
      if (_parms._checkpoint == null) {
        cp =
            new DeepLearningModel(
                dest(),
                _parms,
                new DeepLearningModel.DeepLearningModelOutput(DeepLearning.this),
                _train,
                _valid,
                nclasses());
        cp.model_info().initializeMembers();
      } else {
        final DeepLearningModel previous = DKV.getGet(_parms._checkpoint);
        if (previous == null) throw new IllegalArgumentException("Checkpoint not found.");
        Log.info("Resuming from checkpoint.");
        _job.update(0, "Resuming from checkpoint");

        if (isClassifier() != previous._output.isClassifier())
          throw new H2OIllegalArgumentException(
              "Response type must be the same as for the checkpointed model.");
        if (isSupervised() != previous._output.isSupervised())
          throw new H2OIllegalArgumentException(
              "Model type must be the same as for the checkpointed model.");

        // check the user-given arguments for consistency
        DeepLearningParameters oldP =
            previous._parms; // sanitized parameters for checkpointed model
        DeepLearningParameters newP = _parms; // user-given parameters for restart

        DeepLearningParameters oldP2 = (DeepLearningParameters) oldP.clone();
        DeepLearningParameters newP2 = (DeepLearningParameters) newP.clone();
        DeepLearningParameters.Sanity.modifyParms(
            oldP, oldP2, nclasses()); // sanitize the user-given parameters
        DeepLearningParameters.Sanity.modifyParms(
            newP, newP2, nclasses()); // sanitize the user-given parameters
        DeepLearningParameters.Sanity.checkpoint(oldP2, newP2);

        DataInfo dinfo;
        try {
          // PUBDEV-2513: Adapt _train and _valid (in-place) to match the frames that were used for
          // the previous model
          // This can add or remove dummy columns (can happen if the dataset is sparse and datasets
          // have different non-const columns)
          for (String st : previous.adaptTestForTrain(_train, true, false)) Log.warn(st);
          for (String st : previous.adaptTestForTrain(_valid, true, false)) Log.warn(st);
          dinfo = makeDataInfo(_train, _valid, _parms, nclasses());
          DKV.put(dinfo);
          cp = new DeepLearningModel(dest(), _parms, previous, false, dinfo);
          cp.write_lock(_job);

          if (!Arrays.equals(cp._output._names, previous._output._names)) {
            throw new H2OIllegalArgumentException(
                "The columns of the training data must be the same as for the checkpointed model. Check ignored columns (or disable ignore_const_cols).");
          }
          if (!Arrays.deepEquals(cp._output._domains, previous._output._domains)) {
            throw new H2OIllegalArgumentException(
                "Categorical factor levels of the training data must be the same as for the checkpointed model.");
          }
          if (dinfo.fullN() != previous.model_info().data_info().fullN()) {
            throw new H2OIllegalArgumentException(
                "Total number of predictors is different than for the checkpointed model.");
          }
          if (_parms._epochs <= previous.epoch_counter) {
            throw new H2OIllegalArgumentException(
                "Total number of epochs must be larger than the number of epochs already trained for the checkpointed model ("
                    + previous.epoch_counter
                    + ").");
          }

          // these are the mutable parameters that are to be used by the model (stored in
          // model_info._parms)
          final DeepLearningParameters actualNewP =
              cp.model_info()
                  .get_params(); // actually used parameters for model building (defaults filled in,
                                 // etc.)
          assert (actualNewP != previous.model_info().get_params());
          assert (actualNewP != newP);
          assert (actualNewP != oldP);
          DeepLearningParameters.Sanity.update(actualNewP, newP, nclasses());

          Log.info(
              "Continuing training after "
                  + String.format("%.3f", previous.epoch_counter)
                  + " epochs from the checkpointed model.");
          cp.update(_job);
        } catch (H2OIllegalArgumentException ex) {
          if (cp != null) {
            cp.unlock(_job);
            cp.delete();
            cp = null;
          }
          throw ex;
        } finally {
          if (cp != null) cp.unlock(_job);
        }
      }
      trainModel(cp);

      // clean up, but don't delete weights and biases if user asked for export
      List<Key> keep = new ArrayList<>();
      try {
        if (_parms._export_weights_and_biases
            && cp._output.weights != null
            && cp._output.biases != null) {
          for (Key k : Arrays.asList(cp._output.weights)) {
            keep.add(k);
            for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
              keep.add(vk._key);
            }
          }
          for (Key k : Arrays.asList(cp._output.biases)) {
            keep.add(k);
            for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
              keep.add(vk._key);
            }
          }
        }
      } finally {
        Scope.exit(keep.toArray(new Key[keep.size()]));
      }
    }
Esempio n. 4
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  @Override
  Val apply(Env env, Env.StackHelp stk, AST asts[]) {

    // Execute all args.  Find a canonical frame; all Frames must look like this one.
    // Each argument turns into either a Frame (whose rows are entirely
    // inlined) or a scalar (which is replicated across as a single row).
    Frame fr = null; // Canonical Frame; all frames have the same column count, types and names
    int nchks = 0; // Total chunks
    Val vals[] = new Val[asts.length]; // Computed AST results
    for (int i = 1; i < asts.length; i++) {
      vals[i] = stk.track(asts[i].exec(env));
      if (vals[i].isFrame()) {
        fr = vals[i].getFrame();
        nchks += fr.anyVec().nChunks(); // Total chunks
      } else nchks++; // One chunk per scalar
    }
    // No Frame, just a pile-o-scalars?
    Vec zz = null; // The zero-length vec for the zero-frame frame
    if (fr == null) { // Zero-length, 1-column, default name
      fr = new Frame(new String[] {Frame.defaultColName(0)}, new Vec[] {zz = Vec.makeZero(0)});
      if (asts.length == 1) return new ValFrame(fr);
    }

    // Verify all Frames are the same columns, names, and types.  Domains can vary, and will be the
    // union
    final Frame frs[] = new Frame[asts.length]; // Input frame
    final byte[] types = fr.types(); // Column types
    final int ncols = fr.numCols();
    final long[] espc = new long[nchks + 1]; // Compute a new layout!
    int coffset = 0;

    for (int i = 1; i < asts.length; i++) {
      Val val = vals[i]; // Save values computed for pass 2
      Frame fr0 =
          val.isFrame()
              ? val.getFrame()
              // Scalar: auto-expand into a 1-row frame
              : stk.track(new Frame(fr._names, Vec.makeCons(val.getNum(), 1L, fr.numCols())));

      // Check that all frames are compatible
      if (fr.numCols() != fr0.numCols())
        throw new IllegalArgumentException(
            "rbind frames must have all the same columns, found "
                + fr.numCols()
                + " and "
                + fr0.numCols()
                + " columns.");
      if (!Arrays.deepEquals(fr._names, fr0._names))
        throw new IllegalArgumentException(
            "rbind frames must have all the same column names, found "
                + Arrays.toString(fr._names)
                + " and "
                + Arrays.toString(fr0._names));
      if (!Arrays.equals(types, fr0.types()))
        throw new IllegalArgumentException(
            "rbind frames must have all the same column types, found "
                + Arrays.toString(types)
                + " and "
                + Arrays.toString(fr0.types()));

      frs[i] = fr0; // Save frame

      // Roll up the ESPC row counts
      long roffset = espc[coffset];
      long[] espc2 = fr0.anyVec().espc();
      for (int j = 1; j < espc2.length; j++) // Roll up the row counts
      espc[coffset + j] = (roffset + espc2[j]);
      coffset += espc2.length - 1; // Chunk offset
    }
    if (zz != null) zz.remove();

    // build up the new domains for each vec
    HashMap<String, Integer>[] dmap = new HashMap[types.length];
    String[][] domains = new String[types.length][];
    int[][][] cmaps = new int[types.length][][];
    for (int k = 0; k < types.length; ++k) {
      dmap[k] = new HashMap<>();
      int c = 0;
      byte t = types[k];
      if (t == Vec.T_CAT) {
        int[][] maps = new int[frs.length][];
        for (int i = 1; i < frs.length; i++) {
          maps[i] = new int[frs[i].vec(k).domain().length];
          for (int j = 0; j < maps[i].length; j++) {
            String s = frs[i].vec(k).domain()[j];
            if (!dmap[k].containsKey(s)) dmap[k].put(s, maps[i][j] = c++);
            else maps[i][j] = dmap[k].get(s);
          }
        }
        cmaps[k] = maps;
      } else {
        cmaps[k] = new int[frs.length][];
      }
      domains[k] = c == 0 ? null : new String[c];
      for (Map.Entry<String, Integer> e : dmap[k].entrySet()) domains[k][e.getValue()] = e.getKey();
    }

    // Now make Keys for the new Vecs
    Key<Vec>[] keys = fr.anyVec().group().addVecs(fr.numCols());
    Vec[] vecs = new Vec[fr.numCols()];
    int rowLayout = Vec.ESPC.rowLayout(keys[0], espc);
    for (int i = 0; i < vecs.length; i++)
      vecs[i] = new Vec(keys[i], rowLayout, domains[i], types[i]);

    // Do the row-binds column-by-column.
    // Switch to F/J thread for continuations
    ParallelRbinds t;
    H2O.submitTask(t = new ParallelRbinds(frs, espc, vecs, cmaps)).join();
    return new ValFrame(new Frame(fr.names(), t._vecs));
  }
Esempio n. 5
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 /**
  * Verifies that the actual <code>Object</code> array is not equal to the given array. Array
  * equality is checked by <code>{@link Arrays#deepEquals(Object[], Object[])}</code>.
  *
  * @param array the given array to compare the actual array to.
  * @return this assertion object.
  * @throws AssertionError if the actual <code>Object</code> array is equal to the given one.
  */
 @Override
 public ObjectArrayAssert isNotEqualTo(Object[] array) {
   if (!Arrays.deepEquals(actual, array)) return this;
   failIfCustomMessageIsSet();
   throw failure(unexpectedEqual(actual, array));
 }
Esempio n. 6
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  public int ai_move(int[][] board) {
    if (running) {
      System.out.println(
          "This AI appears to be running multiple ai_moves simultaneously. That can't be right.");
    }
    running = true;
    try {
      // System.out.println(2 + Math.random());
      if (recording) {
        if (out == null) {
          try {
            int ind = 1;
            File f = null;
            while (true) {
              try {
                Scanner sc = new Scanner(new File("AIReplay" + ind + ".txt"));
                ind++;
              } catch (Exception e) {
                break;
              }
            }
            out = new PrintWriter(new File("AIReplay" + ind + ".txt"));
            filename = "AIReplay" + ind + ".txt";
            out.println("AI Version: " + VERSION);
          } catch (Exception e) {
            System.out.println("Could not write to file.");
          }
        }
        fprint(board);
      }
      // if (fml == null) fml = new PrintWriter (new File("fmldebug.txt"));
      if (thisAIIsCheating && max(board) < 8) {
        board[0][0] = GameGUI.win_target;
      }
      if (debug2) sc.nextLine();
      if (debug2) print(board);
      if (debug) System.out.println("New cycle.");
      if (debug) sc.nextLine();
      if (dumbai) name += "Dumby";
      turn++;
      if (!queue.isEmpty()) {
        int temp = queue.removeFirst();
        if (temp > 0) {
          running = false;
          return temp;
        }
      }
      int boardsum = 0;
      for (int i = 0; i < 4; i++) {
        for (int j = 0; j < 4; j++) {
          boardsum += board[i][j];
        }
      }
      boolean report = debug;
      /*if (Math.random() < 0.0001) {
      report = true;
      for (int i = 0; i < 4; i++) {
      	System.out.println(Arrays.toString(board[i]));
      }
      for (int i = 0; i < 4; i++) {
      	System.out.println(movable(board, i));
      }
      System.out.println();
      sc.nextLine();
      }*/
      if (dumbai) {
        if (!name.endsWith("Dumby")) name += "Dumby";
        System.out.println(turn);
        running = false;
        if (turn % 600 == 599) return KeyEvent.VK_DOWN;
        if (turn % 3 == 0) return KeyEvent.VK_UP;
        if (turn % 6 < 3) return KeyEvent.VK_LEFT;
        return KeyEvent.VK_RIGHT;
      } else {
        if (name.indexOf(".") < 0) name += VERSION;
      }
      // gamestart processing
      /*if(board[0][0] == 0) {
      	if (board[1][0] > board[0][1]) {
      		return KeyEvent.VK_UP;
      	}
      	if (board[1][0] < board[0][1]) {
      		return KeyEvent.VK_LEFT;
      	}
      	if (Math.random() < 0.5) return KeyEvent.VK_UP;
      	return KeyEvent.VK_LEFT;
      }*/
      long[] pref = {10, 20, 1, 1}; // LEFT, UP, RIGHT, DOWN

      // check if moving right/down is safe
      boolean occupied = true;

      for (int i = 0; i < 4; i++) {
        if (board[0][i] == 0) occupied = false;
        if (i < 3 && board[0][i] == board[0][i + 1]) occupied = false;
      }
      if (!occupied) {
        // pref[2] -= 100000000;
      }
      occupied = true;
      for (int i = 0; i < 4; i++) {
        if (board[i][0] == 0) occupied = false;
        if (i < 3 && board[i][0] == board[i + 1][0]) occupied = false;
      }
      if (!occupied) {
        // pref[3] -= 100000000;
      }

      pref[0] += 5;
      pref[1] += 5;

      // System.out.println(6 + Math.random());
      // simulate
      sum_board = sum(board);
      delta_sum_board_7over8 = delta(sum_board * 7 / 8);
      if (debug) print(board);
      max_depth = 0;
      for (int m = 0; m < 4; m++) {
        if (debug) System.out.println("Now testing move: " + m);
        int[][] sim = simulate(board, m);
        if (Arrays.deepEquals(sim, board)) {
          if (out != null) out.println("Move " + m + " invalid; skipping");
          if (GameGUI.out != null) GameGUI.out.println("Move " + m + " invalid; skipping");
          continue;
        }
        long worst = (long) 1999999999 * 1000000000;
        long avg = 0;
        int numt = 0;
        for (int i = 0; i < 4; i++) {
          for (int j = 0; j < 4; j++) {
            if (sim[i][j] > 0) continue;
            sim[i][j] = 2;
            long temp = predictor(sim, iter_max / (int) Math.pow((countBlank(sim) + 1), 1.6), 1);
            if (temp < worst) worst = temp;
            avg += 9 * temp;
            sim[i][j] = 4;
            temp = predictor(sim, iter_max / (int) Math.pow((countBlank(sim) + 1), 1.6), 1);
            if (temp < worst) worst = temp;
            avg += temp;
            sim[i][j] = 0;
            numt += 10;
          }
        }
        if (countBlank(sim) == 0) {
          long temp = predictor(sim, iter_max / (int) pow((countBlank(sim) + 1), 2), 1);
          if (temp < worst) worst = temp;
          avg += temp;
          numt++;
        }
        avg /= numt;
        worst = (worst_weight * worst + avg) / (worst_weight + 1);
        if (countBlank(sim) >= 8 && max(board) < 64) worst = avg;
        if (debug || debug2) System.out.println("Move " + m + " final eval: " + worst);
        if (out != null) out.println("Move " + m + " final eval: " + worst);
        if (GameGUI.out != null) GameGUI.out.println("Move " + m + " final eval: " + worst);
        pref[m] += worst;
      }
      if (debug2) System.out.println("Max depth: " + max_depth);
      if (out != null) out.println("Max depth: " + max_depth);
      if (GameGUI.out != null) GameGUI.out.println("Max depth: " + max_depth);

      // System.out.println(5 + Math.random());
      // process output
      int[] dir = new int[4];
      dir[0] = KeyEvent.VK_LEFT;
      dir[1] = KeyEvent.VK_UP;
      dir[2] = KeyEvent.VK_RIGHT;
      dir[3] = KeyEvent.VK_DOWN;
      if (report) System.out.println("Pref: " + Arrays.toString(pref));
      for (int i = 0; i < 4; i++) {
        int best = 0;
        for (int j = 0; j < 4; j++) {
          if (pref[j] > pref[best]) {
            best = j;
          }
        }
        pref[best] = Long.MIN_VALUE;
        if (movable(board, best)) {
          if (report) {
            report = false;
            if (debug) System.out.println("Chosen: " + best);
            if (debug) sc.nextLine();
          }
          // if (pref[best] < -50000000) queue.add(best - 2);
          running = false;
          return dir[best];
        }
        // System.out.println("Unmovable: " + best);
        // System.out.println("Pref: " + Arrays.toString(pref));
      }
      System.out.println("???");
      for (int i = 0; i < 4; i++) {
        System.out.println(Arrays.toString(board[i]));
      }
      // sc.nextLine();
    } catch (Exception e) {
      e.printStackTrace();
    }
    running = false;
    return KeyEvent.VK_LEFT;
  }
Esempio n. 7
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  public long predictor(int[][] board, int iters, int depth) { // returns future value, kinda
    // debnum+=4;
    // System.out.println(Math.random());
    if (depth > max_depth) max_depth = depth;
    // if (max(board) < 64 && depth == 1) return grade4(board);
    int div = 0;
    for (int i = 0; i < 4; i++) {
      int[][] sim = simulate(board, i);
      if (Arrays.deepEquals(sim, board)) continue;
      div += countBlank(sim);
    }
    if (!movable(board, 0) && !movable(board, 1) && !movable(board, 2) && !movable(board, 3)) {
      // if (max(board) == GameGUI.win_target) return grade(board);
      return (long) -1999999999 * 3 * sum(board);
    }
    div *= 2;
    if (div > iters) {
      // debnum-=4;
      return grade4(board);
    }
    iters /= div;
    long best = (long) -1999999999 * 800000000;
    if (debug) print(board);

    for (int m = 0; m < 4; m++) {
      // debnum--;
      int[][] sim = simulate(board, m);
      if (Arrays.deepEquals(sim, board)) continue;
      if (debug) System.out.println("Simulating: " + m);
      long worst = (long) 1999999999 * 800000000;
      long avg = 0;
      int numt = 0;
      for (int i = 0; i < 4; i++) {
        for (int j = 0; j < 4; j++) {
          if (sim[i][j] > 0) continue;
          sim[i][j] = 2;
          long temp = predictor(sim, iters, depth + 1);
          if (temp < worst) worst = temp;
          avg += 9 * temp;
          sim[i][j] = 4;
          temp = predictor(sim, iters, depth + 1);
          if (temp < worst) worst = temp;
          avg += temp;
          sim[i][j] = 0;
          numt += 10;
        }
      }
      if (countBlank(sim) == 0) {
        // System.out.println("??");
        long temp = predictor(sim, iter_max / (int) pow((countBlank(sim) + 1), 2), depth + 1);
        if (temp < worst) worst = temp;
        avg += temp;
        numt++;
      }
      // avg -= worst;
      avg /= numt;
      if (debug) System.out.println("Result: " + worst);
      if ((avg + worst_weight * worst) / (worst_weight + 1) > best)
        best = (worst_weight * worst + avg) / (worst_weight + 1);
      // if (worst > best) best = worst;
      if (div >= 64 && max(board) < 64 && avg > best) best = avg;
    }
    return best;
  }